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CT colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer
screening. Computer-aided detection (CAD) of polyps has improved consistency and sensitivity of virtual colonoscopy
interpretation and reduced interpretation burden. A CAD system typically consists of four stages: (1) image preprocessing
including colon segmentation; (2) initial detection generation; (3) feature selection; and (4) detection
classification. In our experience, three existing problems limit the performance of our current CAD system. First, highdensity
orally administered contrast agents in fecal-tagging CTC have scatter effects on neighboring tissues. The
scattering manifests itself as an artificial elevation in the observed CT attenuation values of the neighboring tissues. This
pseudo-enhancement phenomenon presents a problem for the application of computer-aided polyp detection, especially
when polyps are submerged in the contrast agents. Second, general kernel approach for surface curvature computation in
the second stage of our CAD system could yield erroneous results for thin structures such as small (6-9 mm) polyps and
for touching structures such as polyps that lie on haustral folds. Those erroneous curvatures will reduce the sensitivity of
polyp detection. The third problem is that more than 150 features are selected from each polyp candidate in the third
stage of our CAD system. These high dimensional features make it difficult to learn a good decision boundary for
detection classification and reduce the accuracy of predictions. Therefore, an improved CAD system for polyp detection
in CTC data is proposed by introducing three new techniques. First, a scale-based scatter correction algorithm is applied
to reduce pseudo-enhancement effects in the image pre-processing stage. Second, a cubic spline interpolation method is
utilized to accurately estimate curvatures for initial detection generation. Third, a new dimensionality reduction
classifier, diffusion map and local linear embedding (DMLLE), is developed for classification and false positives (FP)
reduction. Performance of the improved CAD system is evaluated and compared with our existing CAD system (without
applying those techniques) using CT scans of 1186 patients. These scans are divided into a training set and a test set. The
sensitivity of the improved CAD system increased 18% on training data at a rate of 5 FPs per patient and 15% on test
data at a rate of 5 FPs per patient. Our results indicated that the improved CAD system achieved significantly better
performance on medium-sized colonic adenomas with higher sensitivity and lower FP rate in CTC.
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